Adaptability to online learning

Adaptability to Online Learning: Differences Across Types of Students and Academic Subject Areas Di Xu and Shanna Smith Jaggars February 2013 CCRC Working Paper No 54 Address correspondence to: Di Xu Research Associate, Community College Research Center Teachers College, Columbia University 525 West 120th Street, Box 174 New York, NY 10027 212-678-3091 Email: dx2108@tc.columbia.edu Funding for this study was provided by Lumina Foundation for Education, the Bill & Melinda Gates Foundation, and the Association for Institutional Research Abstract Using a dataset containing nearly 500,000 courses taken by over 40,000 community and technical college students in Washington State, this study examines how well students adapt to the online environment in terms of their ability to persist and earn strong grades in online courses relative to their ability to so in face-to-face courses While all types of students in the study suffered decrements in performance in online courses, some struggled more than others to adapt: males, younger students, Black students, and students with lower grade point averages In particular, students struggled in subject areas such as English and social science, which was due in part to negative peer effects in these online courses Table of Contents Introduction Empirical Framework and Data 2.1 Data and Summary Statistics 2.2 Empirical Models Empirical Results 12 3.1 Online Course Enrollments Across Different Subjects 12 3.2 Students’ Online Adaptability Overall 14 3.3 Adaptability Across Different Types of Students 15 3.4 Differences in Online Adaptability Across Course Subject Areas 19 Discussion and Conclusion 23 References 27 Introduction One of the most pronounced trends in higher education over the last decade has been a strong growth in distance education through online coursework (Allen & Seaman, 2010) While the rise of online distance education has expanded learning opportunities for all students, it is often most attractive to nontraditional students, who are more likely to have employment and family obligations that make attending traditional face-to-face classes difficult (Aslanian, 2001) Perhaps as a consequence, online learning enrollments have increased particularly quickly at two-year colleges (Choy, 2002; Parsad & Lewis, 2008), where a large proportion of the population are nontraditional students (Kleinman & Entin, 2002) However, given that most college students received their primary and secondary education in the face-to-face setting, online coursework may represent an adaptation challenge for many In an attempt to understand how readily students adapt to online coursework—that is, the extent to which students perform as well online as they faceto-face—a large body of research has compared outcomes between online and face-toface courses Results have been mixed across studies, with some finding positive results for online learning and others finding negative results (e.g., see Bernard et al., 2004; Zhao, Lei, Yan, Lai, & Tan, 2005; Sitzmann, Kraiger, Stewart, & Wisher, 2006; Jahng, Krug, & Zhang, 2007; U.S Department of Education, 2010) One potential cause for the wide variation in results across studies may lie in the different student populations and course contexts examined in each study Some populations of students—for example, those with more extensive exposure to technology or those who have been taught skills in terms of time-management and self-directed learning—may adapt more readily to online learning than others (Gladieux & Swail, 1999; Jun, 2005; Liu, Gomez, Khan, & Yen, 2007; Muse, 2003; Stewart, Bachman, & Johnson, 2010) In addition, some academic subject areas may lend themselves to highquality online learning experiences more readily than others (Jaggars, 2012) and thus may support students more effectively in their efforts to adapt Below, we discuss in more The National Center for Education Statistics (2002) defines a nontraditional student as one who has any of the following seven risk factors: (1) part-time attendance, (2) full-time employment, (3) delayed postsecondary enrollment, (4) financial independence, (5) having dependents, (6) being a single parent, and (7) not possessing a high school diploma detail how these different contexts could impact the ease with which students adapt to online coursework We begin with a review of research on the impact of student characteristics on online learning performance, focusing on students’ gender, age, ethnicity, and prior academic performance In terms of gender, while several studies have found no differences between males and females in terms of their learning outcomes in online courses (e.g., Astleitner & Steinberg, 2005; Lu, Yu, & Liu, 2003; Ory, Bullock, & Burnaska,1997; Sierra & Wang, 2002; Yukselturk & Bulut, 2007), others have found that women perform significantly better than men (e.g., Chyung, 2001; Gunn, McSporran, Macleod, & French, 2003; Price, 2006; Rovai & Baker, 2005; Sullivan, 2001; Taplin & Jegede, 2001) To explain the stronger performance of women within their study of online courses, McSporran and Young (2001) examined course observation and student survey data They concluded that the women in their sample were more motivated, more adept at communicating online, and more effective in scheduling their learning In contrast, male participants accessed fewer course website pages and fewer discussion forum posts; they also had poorer time management skills and tended to be overconfident in terms of their ability to complete learning tasks and assignments The notion that women may perform more strongly than men within online courses should not be particularly surprising, given that women tend to have stronger educational outcomes across a variety of contexts and timeframes For example, women are more likely to graduate from high school (Swanson, 2004, Heckman & LaFontaine, 2007), and among students who attend college, women are more likely to earn a degree (Diprete & Buchmann 2006; Goldin, Katz, & Kuziemko, 2006) A more compelling question for online researchers may be: Do women more easily adapt to online courses than men? Put another way, is the gap between male and female performance wider or narrower within the online context than within the face-to-face classroom context? Thus far, however, the moderating role of gender in terms of students’ adaptability to online learning has been left unexplored Similarly, Black and Hispanic students may perform more poorly than White students in online courses (Newell, 2007) If this is so, the pattern would certainly be due in part to the fact that Black and Hispanic students tend to perform more poorly in college overall, given that they are systematically disadvantaged in terms of the quality of their primary and secondary schooling (Feldman, 1993; Allen, 1997; DuBrock, 2000; Wiggam, 2004) No studies thus far have explored the moderating role of ethnicity in terms of student adaptability to online courses—that is, no studies we are aware of have examined whether the ethnic minority performance gap is exacerbated by online coursework However, some researchers (e.g., Gladieux & Swail, 1999) have raised concerns that online learning could widen the postsecondary access gap between students of color and White students because of inequities in terms of at-home computer and Internet equipment For example, in 2009, only 52 percent of African Americans and 47 percent of Hispanics had high-speed Internet access at home (Rainie, 2010) Such disadvantages in terms of at-home technological infrastructure could affect these students’ ability to perform well in online courses In terms of student age, some studies have found no relationship between age and satisfaction or performance in online learning (e.g., Biner, Summers, Dean, Bink, Anderson, & Gelder, 1996; Osborn, 2001; Wang & Newlin, 2002; Willging & Johnson, 2004), while others have found that older students are more likely to complete online courses than their younger counterparts (Dille & Mezack, 1991; Willis, 1992; Didia & Hasnat, 1998; Wojciechowski & Palmer, 2005) For example, in one study of online learning (Dille & Mezack, 1991), the average age of successful students was 28, as opposed to 25 for non-successful students Colorado and Eberle (2010) have argued that older students’ success in online learning may be due to increases with age in levels of rehearsal, elaboration, critical thinking, and metacognitive self-regulation, each of which may contribute to success in online coursework The notion that older students may perform more successfully than younger students in online courses is intriguing, given that older college students tend to have poorer academic outcomes overall Perhaps due to family and employment obligations (Choy & Premo, 1995; Horn & Carroll, 1996), older community college students are less likely than younger students to earn any credential or to transfer to a four-year university (Calcagno, Crosta, Bailey, & Jenkins, 2007) If older students indeed adapt well to the online environment, then online learning should be encouraged among this population, as it would provide them with expanded postsecondary access and an academic advantage that they may not otherwise have (Hyllegard, Deng, & Carla, 2008) In contrast to the large volumes of studies examining gender, ethnicity, and age as predictors of online success, very few studies (e.g., Hoskins & Hooff, 2005; Figlio, Rush, & Yin, 2010) have examined the role of students’ pre-existing academic ability Yet students with weaker academic preparation may also have insufficient time management and self-directed learning skills, both of which are thought to be critical to success in online and distance education (e.g., Bambara, Harbour, & Davies, 2009; Ehrman, 1990; Eisenberg & Dowsett, 1990; Liu et al., 2007) Thus, while one would expect students with lower levels of academic preparation to fare more poorly in any course compared to their better prepared peers, one might expect that performance gap to be even wider in the online context Indeed, a recent experimental study comparing learning outcomes between online and face-to-face sections of an economics course (Figlio et al., 2010) found no significant difference between the two course formats among students with higher prior GPAs; however, among those with lower prior GPAs, those in the online condition scored significantly lower on in-class exams than did those in the face-to-face sections That is, low-GPA students had more difficulty adapting to the online context than did high-GPA students Overall, the research on the impact of student characteristics on online success indicates that patterns of performance in online courses mirror those seen in postsecondary education overall: Women and White students are likely to perform more strongly online than their counterparts However, most studies have focused on student characteristics as a straightforward predictor (e.g., women perform better than men within an online course?) rather than focusing on their potential influence on students’ adaptability to online learning (e.g., women adapt more easily to online learning than men, leading to a wider gender gap in online courses than in face-to-face courses?) As a result, there is limited evidence in terms of how the continued expansion of online learning may differentially impact different types of students Regardless of students’ own characteristics, their adaptability to online learning may also differ by academic subject, as online courses might be more engaging or effective in some subject areas than in others For instance, it may be more difficult to create effective online materials, activities, or assignments in fields that require a high degree of hands-on demonstration and practice, intensive instructor-student interaction, or immediate personalized feedback In support of the notion that the effectiveness of online learning may differ across subject areas, a recent qualitative study (Jaggars, 2012) examined course subjects that students preferred to take online rather than face-to-face Students reported that they preferred to take “difficult” courses (with mathematics being a frequently cited example) in a face-to-face setting, while “easy” courses could be taken online Students also explicitly identified some subject areas that they felt were “poorly suited to the online context” (p 8), such as laboratory science courses and foreignlanguage courses Outside of these qualitative data, however, the field has no information regarding which subject areas may be more or less effectively taught online In this paper, we examine whether student adaptability to online learning (that is, students’ performance in online courses compared to their own performance in face-toface courses) varies across student characteristics and academic subject areas Information on the moderating role of student characteristics can help institutions market online courses more aggressively to subgroups that are likely to benefit more strongly from them, while devising support systems for subgroups that may experience more difficulties in an online learning environment Information on course subjects that are more or are less well-suited to online learning may help institutions allocate resources for online course development more effectively To investigate these issues, we take advantage of a large administrative dataset including nearly 500,000 online and face-to-face courses taken by more than 40,000 degree-seeking students who initially enrolled in one of Washington State’s 34 community or technical colleges during the fall term of 2004 Using a subsample of the same dataset, we (Xu & Jaggars, 2012) previously explored the overall impact of online learning on student outcomes through an instrumental variable (IV) approach and found robust negative estimates on both course persistence and (among course completers) course grade, indicating that many students had difficulty adapting to the online context Specifically, we used the distance from a student’s home to college as an instrument for the student’s likelihood of enrolling in an online rather than face-to-face section of a given course To satisfy the assumptions underlying the IV and course fixed effects approach, the authors limited the sample to Washington residents enrolled in an academic transfer track and to courses offering both online and faceto-face sections Although the empirical strategy enabled us to effectively isolate the causal impact of alternative delivery formats on student performance, the sample constraints imposed by the IV approach resulted in a student sample that was fairly homogeneous in academic capacity, motivation, and type of courses enrolled As a result, it is possible that the estimates in that study were driven by particular student or subject subgroups, while other subgroups may have had a stronger capacity to adapt to online coursework Thus, in this study, we include all the courses taken by the entire degree-seeking student population and employ an individual fixed effects approach to examine whether the gap between online and face-to-face outcomes is stronger or weaker within various subgroups The results show that males, younger students, Black students, and students with lower levels of prior academic performance had more difficulty adapting to online courses The remainder of this paper is organized as follows: section describes the database and introduces our empirical strategies; section presents the results regarding both the overall impacts of online courses and the heterogeneous impacts by subgroups; and section discusses findings from the current study and presents policy recommendations Empirical Framework and Data 2.1 Data and Summary Statistics Primary analyses were performed on a dataset containing 51,017 degree-seeking students who initially enrolled in one of Washington State’s 34 community or technical colleges during the fall term of 2004 These first-time college students were tracked through the spring of 2009 for 19 quarters of enrollment, or approximately five years The dataset, provided by the Washington State Board of Community and Technical Colleges (SBCTC), included information on student demographics, institutions attended, and transcript data on course enrollments and performance This sample does not include students who were dual-enrolled during the fall term of 2004 (N = 6,039) There are four quarters in each academic year, which starts in summer and ends in spring We also refer to a quarter as a term adaptability to online learning lends them a slight advantage in online courses in comparison with their younger counterparts Finally, to investigate the possibility that lower levels of academic skill may moderate the effect of online learning, we initially used a variable indicating whether the student had ever enrolled in a remedial course (termed an ever-remedial student) The pvalue for the F test on the interaction term (p = 078) was significant for course persistence at the level and significant for course grade at the 05 level (p = 017), indicating that students who entered college with lower academic preparedness had more difficulty adapting to online courses However, it is worth noting that one problem with using remedial enrollment as a proxy for academic skill level is that many students assigned to remediation education may not actually take the courses (e.g., see Roksa et al., 2009; Bailey, Jeong, & Cho, 2010) Thus the “non-remedial” population may in fact include some students who entered college academically underprepared but who skipped remediation Moreover, a high proportion of students assigned to remediation drop out of college in their first or second semester (Bailey et al., 2010; Jaggars & Hodara, 2011); thus, the student population narrows in subsequent semesters to only those who are the most motivated and well equipped to succeed in school As a result, the estimates presented in Table may underestimate the interaction effects between initial academic preparedness and course delivery format To investigate the role of academic capacity in another way, we conducted an additional analysis using students’ GPA in their face-to-face courses in the initial term as a more precise measure of academic skill and motivation 12 We used face-to-face GPA for two reasons: (1) GPA based on only one type of course format eliminated the impact of different course formats on GPA outcomes; and (2) face-to-face GPA represented academic performance in the bulk of courses taken in students’ first semesters, as relatively few students took online courses in their first semester (7 percent) and very few 12 The drawback to this indicator is that students without a valid first-term face-to-face GPA were dropped from the sample These students may have withdrawn from all courses, earned only remedial credits (which not award GPA points), or completed only online courses in their first semester This exclusion resulted in a loss of 13 percent of the overall course sample We were concerned that this reduced sample could differ from the original sample in terms of the overall impacts of online format on course outcomes We checked this possibility by re-conducting the overall online impacts analysis on this subsample, and results were nearly identical to those presented in Table (e.g., estimates based on model are coefficientpersistence = −0.046, p < 01; coefficientgrade = −0.275, p < 01) 18 took all their courses online in that term (3 percent) As shown in Table 4, the interactive effect of academic capacity was magnified when using the GPA measure; p-values for the interaction terms were significant at the p < 01 level for both course persistence and course grade, and the gap of the coefficients between the two groups was even wider compared to those in the ever-remedial model The results from both the ever-remedial and GPA interaction models indicate that students with stronger academic capacity tended to be less negatively affected by online courses, while students with weaker academic skill were more strongly negatively affected The interaction also indicates that the gap in course performance between highand low-skill students tended to be stronger in online courses than in face-to-face courses One potential concern with the student subgroup analyses is that heterogeneity in estimates could be due to subgroup differences in subject-area selection For example, the observed interaction between gender and online adaptability could be due to a female propensity to choose majors that happen to have higher-quality online courses Accordingly, we tested the interactions between student characteristics and online adaptability within each academic subject area Although not always significant across all subjects, the size and direction of the coefficients generally echoed those presented in Table 4: Males, younger students, students with lower levels of academic skill, and Black students were likely to perform particularly poorly in online courses relative to their performance in face-to-face courses 3.4 Differences in Online Adaptability Across Course Subject Areas In order to explore whether students adapt to online learning more effectively in some academic subject areas than in others, we included a set of interaction terms between subject area and online course format into specification 3, 13 and examined the joint significance of all the interaction terms through an F test The interaction test was strong and significant for both course persistence, F = 6.01, p < 001, and course grade, F = 13.87, p < 001, indicating that student adaptability to online learning did vary by academic subject area To decompose the interaction effects, we separately estimated the coefficient for online learning within each subject area using Equation Results are 13 All models also include time fixed effects and academic subject fixed effects, where the latter is applied to those subjects that have multiple sub-disciplines, shown in Table 19 presented in Table 5, where each cell represents a separate regression using individual and time fixed effects; fixed effects are also included for academic subject areas that included multiple sub-disciplines (as shown above in Table 2) Table Individual Fixed-Effect Estimate for Online Learning, by Course Subject (restricted to academic subjects with at least percent online enrollment) Subject Overall Course Persistence −0.043 (0.002)*** Course Grade −0.267 (0.008)*** Social Science Education Computer Science Humanities English Mass Communication Applied Knowledge Applied Profession Natural Science Health & PE Math p-value for the interaction terms −0.064 (0.005)*** −0.016 (0.013) −0.024 (0.008)*** −0.052 (0.012)*** −0.079 (0.006)*** −0.039 (0.038) −0.036 (0.007)*** −0.027 (0.004)*** −0.030 (0.007)*** −0.009 (0.010) −0.065 (0.016)*** < 001 −0.308 (0.018)*** −0.337 (0.059)*** −0.221 (0.041)*** −0.190 (0.046)*** −0.394 (0.023)*** −0.277 (0.159)* −0.322(0.030)*** −0.211 (0.018)*** −0.159 (0.025)*** −0.300 (0.046)*** −0.234 (0.056)*** < 001 Note Standard errors for all the models are clustered at the student level All models also include time fixed effects and academic subject fixed effects, where the latter is applied to subjects that have multiple disciplines as presented in Table ***Significant at the percent level **Significant at the percent level *Significant at the 10 percent level Overall, every academic subject area showed negative coefficients for online learning in terms of both course persistence and course grade However, some had relatively weak coefficients, and three subject areas had insignificant coefficients for the outcome of persistence The subject areas in which the negative coefficients for online learning were weaker than average in terms of both course persistence and course grades (indicating that students were relatively better able to adapt to online learning in these subjects) were computer science, the applied professions, and natural science One potential explanation for the variation in student adaptability across subject areas concerns the type of student who took online courses in each subject area While we controlled for the overall effects of student characteristics in the above model, we did not control for how those characteristics may have impacted differences between online and face-to-face performance To so, we added into the model interaction terms between course delivery format and the four key individual characteristics (i.e., gender, ethnicity, first-term face-to-face GPA, and age) The interaction terms between subject area and 20 course format reduced in size but remained significant for both course persistence (F = 2.55, p = 004) and course grade (F = 5.55, p < 001), indicating that the variation across subject areas in terms of online course effectiveness persisted after taking into account the characteristics of students in each subject area and how well those types of students adapted to online learning Another potential source of variation in online impacts across academic subjects is peer effects based on the macro-level composition of students in each subject area While the models above control for how an individual’s characteristics affect his or her own performance, they not control for how the individual’s performance is affected by the other students in his or her courses Descriptive supplemental analyses indicate that peer effects could be a salient issue: Students with higher first-term GPAs in face-to-face courses (hereafter referred to as first-term f2f GPA) tended to cluster their course enrollments in subject areas with weaker negative coefficients for online learning While the average first-term f2f GPA across our sample was 2.95, it was higher among course enrollees in the natural sciences (3.02), computer science (3.02), and the applied professions (3.03) In the natural science sub-discipline of physics, in which course enrollees had a particularly high first-term f2f GPA (3.12), the negative coefficients for online learning in terms of both course persistence (p = 306) and course grade (p = 802) were no longer significant In contrast, subject areas with enrollees who had low firstterm f2f GPAs (e.g., 2.89 in English and 2.82 in social science) had stronger negative estimates for online learning, as shown in Table These descriptive comparisons suggest that a given student is exposed to higher performing peers in some subject areas and lower performing peers in others and that this could affect his or her own adaptability to online courses in each subject area To explore the potential impact of peer effects in terms of how well students adapt to online courses in a given subject area, we created an indicator, online-at-risk, defined as students who are academically less prepared (with a first-term f2f GPA below 3.0) and who also have at least one of the other demographic characteristics indicating greater risk of poor online performance (i.e., being male, younger, or Black) We then calculated the proportion of online-at-risk students for each course and interacted this variable with the course delivery format The interaction terms were negative and significant at the p < 01 21 level for both course persistence and course grade, indicating that an individual student’s performance penalty in an online course was stronger when the student’s classmates were having difficulty adapting to the online context To provide a clear illustration of the peer effect interaction, we estimated the online learning coefficient separately for courses where 75 percent or more students were online-at-risk and for courses where 25 percent or fewer were online-at-risk In courses where 75 percent or more were online-at-risk (N = 25,128), the negative coefficients for online delivery were strong: −0.064 (p < 01) for course persistence and −0.359 (p < 01) for course grade In contrast, in courses where 25 percent or fewer students were onlineat-risk (N = 201,539), the negative impacts were nearly halved, to −0.035 (p < 01) for course persistence and −0.231 (p < 01) for course grade After controlling for student characteristics in all feasible ways, including peer effects, the interaction terms between academic subject areas and course delivery format were still significant at the p < 01 level for both course persistence and course grade, indicating that there may have been intrinsic differences between subject areas in terms of the effectiveness of their online courses To provide a clearer understanding of this pattern, we restricted our analysis of each academic subject to course enrollments (N = 39, 614) among the group of students who adapted best to the online delivery format— i.e., students who were female, older, non-Black, and had a GPA above or equal to 3.0 in their face-to-face courses in the initial term of college Within this highly adaptable subsample with peer effects controlled, any remaining significant negative online coefficients in a given subject may indicate that the particular subject area is intrinsically difficult to adapt to the online context Within this subsample, the online coefficients were non-significant for both course outcomes in most of the subject areas, but they remained significantly and substantially negative in the subject areas of social science (N = 3,136; Coefficientpersistence= −0.050, p < 01; Coefficientgrade = −0.195, p < 01) and applied professions (N = 12,924; Coefficientpersistence= −0.020, p = 0.01; Coefficientgrade = −0.135, p < 01) 22 Discussion and Conclusion In order to understand whether particular student subgroups may have more or less difficulty adapting to online coursework, the current study analyzed student performance across a large swath of online and face-to-face courses using a statewide community college dataset Overall, the online format had a significantly negative relationship with both course persistence and course grade, indicating that the typical student had difficulty adapting to online courses While this negative sign remained consistent across all subgroups, the size of the negative coefficient varied significantly across subgroups Specifically, we found that males, Black students, and students with lower levels of academic preparation experienced significantly stronger negative coefficients for online learning compared with their counterparts, in terms of both course persistence and course grade These results provide support for the notion that students are not homogeneous in their adaptability to the online delivery format and may therefore have substantially different outcomes for online learning (Muse, 2003; Wiggam, 2004; Hoskins & van Hooff, 2005; Jun, 2005; Stewart et al., 2010) These patterns also suggest that performance gaps between key demographic groups already observed in face-to-face classrooms (e.g., gaps between male and female students, and gaps between White and ethnic minority students) are exacerbated in online courses This is troubling from an equity perspective: If this pattern holds true across other states and educational sectors, it would imply that the continued expansion of online learning could strengthen, rather than ameliorate, educational inequity We also found that older students adapted more readily to online courses than did younger students This finding is intriguing, given that older college students tend to have poorer academic outcomes overall While older students still did more poorly in online than in face-to-face courses, for this population a slight decrement in performance may represent a rational trade-off: Given that a majority of older students assume working and family responsibilities, without the flexibility of online learning, they would have to take fewer courses each semester (Jaggars, 2012) As such, older students may be willing to trade the ability to take an additional course for slightly poorer performance in that course 23 In addition to variation across types of students, we also found that the relative effects of online learning varied across academic subject areas While there may be intrinsic characteristics that render some subject areas better suited than others to online learning, our results also suggest that the macro-level composition of enrollments within a particular subject area impacts the effectiveness of its online courses, in two ways First, different types of students tend to cluster systematically into different academic subject areas While some areas attract students with a strong ability to adapt to online coursework, others attract students who not adapt well Second, regardless of a particular student’s own adaptability to the online environment, her performance in an online course may suffer if her classmates adapt poorly English and social science were two academic subjects that seemed to attract a high proportion of less-adaptable students, thereby introducing negative peer effects Perhaps in online courses with a high proportion of less-adaptable students, interpersonal interactions and group projects are more challenging and less effective, which then negatively impacts everyone’s course performance; or perhaps instructors devote more attention to students who are struggling most to adapt, leaving the remaining students with less support in their own efforts to adapt Future research examining the mechanisms of peer effects within online courses may wish to examine these possibilities Outside of the effects of self and peer adaptability to online courses in general, two academic subject areas appeared intrinsically more difficult for students in the online context: the social sciences (which include anthropology, philosophy, and psychology) and the applied professions (which include business, law, and nursing) Perhaps these subjects require a high degree of hands-on demonstration and practice, making it more difficult for instructors to create effective online materials, activities, or assignments Or perhaps the learning process in these subjects requires intensive student–instructor interactions and student–student discussions, which studies have suggested are more difficult to effectively implement in the online context (e.g., Bambara et al., 2009; Jaggars, 2012) Overall, our findings indicate that the typical student has some difficulty adapting to online courses, but that some students adapt relatively well while others adapt very poorly To improve student performance in online courses, colleges could take at least 24 four distinct approaches: screening, scaffolding, early warning, and wholesale improvement First, in terms of screening, colleges could redefine online learning as a student privilege rather than a right For example, they could bar students from enrolling in online courses until they demonstrate that they are likely to adapt well to the online context (for example, by earning a 3.0 or better GPA, or by successfully completing a workshop on online learning skills) However, this strategy may disadvantage some students, particularly older students, who legitimately require the flexibility of online coursework; what is worse, it could cause drops in enrollments if students interested in online learning are enticed to schools that not have such screening requirements The variation across student demographic groups also has a consequence for individual academic departments, as more-adaptable students tend to cluster in some academic areas while less-adaptable students cluster in others As a variant on the screening strategy, colleges might also consider an online course allocation strategy For example, colleges might consider limiting or eliminating the supply of online sections for course subjects in which a considerable proportion of students are at risk to adapt poorly As is shown in Table 2, many colleges have already followed this approach by offering very few online courses in developmental education, where a large proportion of students are academically underprepared A second strategy is scaffolding: incorporating the teaching of online learning skills into online courses in which less-adaptable students tend to cluster, such as English composition This strategy would require the college to work with instructors to develop materials and assignments that develop online learning skills and deploy them in the selected courses A potential drawback to this strategy, however, is that some students might enroll in several “scaffolded” courses and become bored and frustrated with the now-unnecessary online learning skill exercises A third possibility is incorporating early warning systems into online courses in order to identify and intervene with students who are having difficulty adapting For example, if a student fails to sign in to the online system, or fails to turn in an early ungraded assignment, the system could generate a warning for the instructor or for the college’s counseling department, who could in turn call the student to see if he or she is 25 experiencing problems and discuss potential supports or solutions Early warning systems are becoming increasingly popular but may require a substantial outlay of up-front costs, as well as faculty or counselor time The first three strategies assume that the majority of online courses remain static in their quality, while the students enrolled in them improve their online skills The fourth strategy, improvement, would instead focus on improving the quality of all online courses taught at the college, to ensure that their learning outcomes are equal to those of face-toface courses, regardless of the composition of the students enrolled Such an improvement strategy would require substantial new investments in course design, faculty professional development, learner and instructor support, and systematic course evaluations Although many students face challenges in adapting to online learning, online coursework represents an indispensible strategy in postsecondary education, as it improves flexibility for both students and institutions and expands educational opportunities among students who are balancing school with work and family demands Our results may help stakeholders involved in the planning, teaching, or supervision of online courses to consider strategies that will improve student outcomes in these courses However, our study addresses only the community college context, and in only one state Additional research in other states, and particularly in the four-year college setting, is needed to gain further insight into the impact of individual characteristics and course subject areas on students’ ability to adapt to online courses 26 References Allen, D (1997, May) The hunger factor in student retention: An analysis of motivation Paper presented at the Annual Forum of the Association for Institutional Research, Orlando, FL Allen, I E., & Seaman, J (2010) Class differences: Online education in the United States, 2010 Needham, MA: Babson Survey Research Group Aslanian, C (2001) You’re never too old: 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